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Proceedings Paper

Achieving semantic coupling in the domain of high-dimensional video indexing application
Author(s): Ankush Mittal; Loong-Fah Cheong
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Paper Abstract

In this paper, an adequately domain-independent approach is presented where local features can characterize multimedia data using Neural Networks (ANN) and Support Vector Machines (SVM). In our previous work, we have shown that classification in content-based retrieval requires non- linear mapping of feature space. This can normally be accomplished by ANN and SVM. However, they inherently lack the capability to deal with meaningful feature evaluation and large dimensional feature space in the sense that they are inaccurate and slow. These defects can be overcome by employing meaningful feature selection on the basis of discriminative capacity of a feature. The experiments on database consisting of real video sequences show that the speed and accuracy of SVM can be improved substantially using this technique, while execution time can be substantially reduced for ANN. The comparison also shows that improved SVM turns out to be a better choice than ANN. Finally, it is shown that generalization in learning is not affected by reducing the dimension of the feature space by our method.

Paper Details

Date Published: 4 April 2001
PDF: 11 pages
Proc. SPIE 4305, Applications of Artificial Neural Networks in Image Processing VI, (4 April 2001); doi: 10.1117/12.420931
Show Author Affiliations
Ankush Mittal, National Univ. of Singapore (Singapore)
Loong-Fah Cheong, National Univ. of Singapore (Singapore)

Published in SPIE Proceedings Vol. 4305:
Applications of Artificial Neural Networks in Image Processing VI
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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